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Optimal Community Energy Storage System Operation in a Multi-Power Consumer System: A Stackelberg Game Theory Approach

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  • Gyeong Ho Lee

    (Information & Electronic Research Institute, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34051, Republic of Korea)

  • Junghyun Lee

    (School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34051, Republic of Korea)

  • Seong Gon Choi

    (School of Information and Communication Engineering, Chungbuk University, Cheongju-si 28644, Republic of Korea)

  • Jangkyum Kim

    (Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea)

Abstract

The proliferation of community energy storage systems (CESSs) necessitates effective energy management to address financial concerns. This paper presents an efficient energy management scheme for heterogeneous power consumers by analyzing various cost factors relevant to the power system. We propose an authority transaction model based on a multi-leader multi-follower Stackelberg game, demonstrating the existence of a unique Stackelberg equilibrium to determine optimal bidding prices and allocate authority transactions. Our model shows that implementing a CESS can reduce total electricity costs by 16% compared to the conventional case that does not account for authority transactions among CESS users, highlighting its effectiveness in practical power systems.

Suggested Citation

  • Gyeong Ho Lee & Junghyun Lee & Seong Gon Choi & Jangkyum Kim, 2024. "Optimal Community Energy Storage System Operation in a Multi-Power Consumer System: A Stackelberg Game Theory Approach," Energies, MDPI, vol. 17(22), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5683-:d:1520334
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    References listed on IDEAS

    as
    1. Song, Ziyou & Feng, Shuo & Zhang, Lei & Hu, Zunyan & Hu, Xiaosong & Yao, Rui, 2019. "Economy analysis of second-life battery in wind power systems considering battery degradation in dynamic processes: Real case scenarios," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    2. Boram Kim & Sunghwan Bae & Hongseok Kim, 2017. "Optimal Energy Scheduling and Transaction Mechanism for Multiple Microgrids," Energies, MDPI, vol. 10(4), pages 1-17, April.
    3. Maheshwari, Arpit & Paterakis, Nikolaos G. & Santarelli, Massimo & Gibescu, Madeleine, 2020. "Optimizing the operation of energy storage using a non-linear lithium-ion battery degradation model," Applied Energy, Elsevier, vol. 261(C).
    4. Terlouw, Tom & AlSkaif, Tarek & Bauer, Christian & van Sark, Wilfried, 2019. "Multi-objective optimization of energy arbitrage in community energy storage systems using different battery technologies," Applied Energy, Elsevier, vol. 239(C), pages 356-372.
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